1. Introduction
   • Features
2. Installation
   • Setup and Documentation
3. Project Details
   • Demonstration
   • Directory Details
   • How to Add a new Model
4. License

Project Insight is designed to create NLP as a service with code base for both front end GUI (streamlit) and backend server (FastApi) the usage of transformers models on various downstream NLP task.

The downstream NLP tasks covered:

• News Classification

• Entity Recognition

• Sentiment Analysis

• Summarization

• Information Extraction To Do

The user can select different models from the drop down to run the inference.

The users can also directly use the backend fastapi server to have a command line inference.

• Python Code Base: Built using Fastapi and Streamlit making the complete code base in Python.
• Expandable: The backend is desinged in a way that it can be expanded with more Transformer based models and it will be available in the front end app automatically.
• Micro-Services: The backend is designed with a microservices architecture, with dockerfile for each service and leveraging on Nginx as a reverse proxy to each independently running service.
  • This makes it easy to update, manitain, start, stop individual NLP services.

• Clone the Repo.
• Run the Docker Compose to spin up the Fastapi based backend service.
• Run the Streamlit app with the streamlit run command.

1. Download the models

   • Download the models from here
   • Save them in the specific model folders inside the src_fastapi folder.

2. Running the backend service.

   • Go to the src_fastapi folder
   • Run the Docker Compose comnand

   $ cd src_fastapi
   src_fastapi:~$ sudo docker-compose up -d

3. Running the frontend app.

   • Go to the src_streamlit folder
   • Run the app with the streamlit run command

   $ cd src_streamlit
   src_streamlit:~$ streamlit run NLPfily.py

4. Access to Fastapi Documentation: Since this is a microservice based design, every NLP task has its own seperate documentation

   • News Classification: http://localhost:8080/api/v1/classification/docs
   • Sentiment Analysis: http://localhost:8080/api/v1/sentiment/docs
   • NER: http://localhost:8080/api/v1/ner/docs
   • Summarization: http://localhost:8080/api/v1/summary/docs

• Front End: Front end code is in the src_streamlit folder. Along with the Dockerfile and requirements.txt

• Back End: Back End code is in the src_fastapi folder.

  • This folder contains directory for each task: Classification, ner, summary...etc
  • Each NLP task has been implemented as a microservice, with its own fastapi server and requirements and Dockerfile so that they can be independently mantained and managed.
  • Each NLP task has its own folder and within each folder each trained model has 1 folder each. For example:

  - sentiment
      > app
          > api
              > distilbert
                  - model.bin
                  - network.py
                  - tokeniser files
              >roberta
                  - model.bin
                  - network.py
                  - tokeniser files
  

  • For each new model under each service a new folder will have to be added.

  • Each folder model will need the following files:

    • Model bin file.
    • Tokenizer files
    • network.py Defining the class of the model if customised model used.

  • config.json: This file contains the details of the models in the backend and the dataset they are trained on.

1. Fine Tune a transformer model for specific task. You can leverage the transformers-tutorials

2. Save the model files, tokenizer files and also create a network.py script if using a customized training network.

3. Create a directory within the NLP task with directory_name as the model name and save all the files in this directory.

4. Update the config.json with the model details and dataset details.

5. Update the <service>pro.py with the correct imports and conditions where the model is imported. For example for a new Bert model in Classification Task, do the following:

   • Create a new directory in classification/app/api/. Directory name bert.

   • Update config.json with following:

     "classification": {
     "model-1": {
         "name": "DistilBERT",
         "info": "This model is trained on News Aggregator Dataset from UC Irvin Machine Learning Repository. The news headlines are classified into 4 categories: **Business**, **Science and Technology**, **Entertainment**, **Health**. [New Dataset](https://archive.ics.uci.edu/ml/datasets/News+Aggregator)"
     },
     "model-2": {
         "name": "BERT",
         "info": "Model Info"
     }
     }

   • Update classificationpro.py with the following snippets:

     Only if customized class used

     from classification.bert import BertClass

     Section where the model is selected

     if model == "bert":
         self.model = BertClass()
         self.tokenizer = BertTokenizerFast.from_pretrained(self.path)

This project is licensed under the GPL-3.0 License - see the LICENSE.md file for details